Multiomic biomarkers after cardiac arrest.

IF 2.8 Q2 CRITICAL CARE MEDICINE Intensive Care Medicine Experimental Pub Date : 2024-09-27 DOI:10.1186/s40635-024-00675-y
Victoria Stopa, Gabriele Lileikyte, Anahita Bakochi, Prasoon Agarwal, Rasmus Beske, Pascal Stammet, Christian Hassager, Filip Årman, Niklas Nielsen, Yvan Devaux
{"title":"Multiomic biomarkers after cardiac arrest.","authors":"Victoria Stopa, Gabriele Lileikyte, Anahita Bakochi, Prasoon Agarwal, Rasmus Beske, Pascal Stammet, Christian Hassager, Filip Årman, Niklas Nielsen, Yvan Devaux","doi":"10.1186/s40635-024-00675-y","DOIUrl":null,"url":null,"abstract":"<p><p>Cardiac arrest is a sudden cessation of heart function, leading to an abrupt loss of blood flow and oxygen to vital organs. This life-threatening emergency requires immediate medical intervention and can lead to severe neurological injury or death. Methods and biomarkers to predict neurological outcome are available but lack accuracy. Such methods would allow personalizing healthcare and help clinical decisions. Extensive research has been conducted to identify prognostic omic biomarkers of cardiac arrest. With the emergence of technologies allowing to combine different levels of omics data, and with the help of artificial intelligence and machine learning, there is a potential to use multiomic signatures as prognostic biomarkers after cardiac arrest. This review article delves into the current knowledge of cardiac arrest biomarkers across various omic fields and suggests directions for future research aiming to integrate multiple omics data layers to improve outcome prediction and cardiac arrest patient's care.</p>","PeriodicalId":13750,"journal":{"name":"Intensive Care Medicine Experimental","volume":"12 1","pages":"83"},"PeriodicalIF":2.8000,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11436561/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Intensive Care Medicine Experimental","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s40635-024-00675-y","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CRITICAL CARE MEDICINE","Score":null,"Total":0}
引用次数: 0

Abstract

Cardiac arrest is a sudden cessation of heart function, leading to an abrupt loss of blood flow and oxygen to vital organs. This life-threatening emergency requires immediate medical intervention and can lead to severe neurological injury or death. Methods and biomarkers to predict neurological outcome are available but lack accuracy. Such methods would allow personalizing healthcare and help clinical decisions. Extensive research has been conducted to identify prognostic omic biomarkers of cardiac arrest. With the emergence of technologies allowing to combine different levels of omics data, and with the help of artificial intelligence and machine learning, there is a potential to use multiomic signatures as prognostic biomarkers after cardiac arrest. This review article delves into the current knowledge of cardiac arrest biomarkers across various omic fields and suggests directions for future research aiming to integrate multiple omics data layers to improve outcome prediction and cardiac arrest patient's care.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
心脏骤停后的多组学生物标志物
心脏骤停是指心脏功能突然停止,导致重要器官突然失去血流和氧气。这种危及生命的紧急情况需要立即进行医疗干预,并可能导致严重的神经损伤或死亡。目前已有预测神经系统结果的方法和生物标志物,但缺乏准确性。这种方法可以实现个性化医疗保健,并有助于临床决策。为确定心脏骤停的预后生物标志物,已经开展了大量研究。随着可将不同层次的 omics 数据结合起来的技术的出现,在人工智能和机器学习的帮助下,有可能将多组学特征用作心脏骤停后的预后生物标志物。这篇综述文章深入探讨了目前不同物组学领域中有关心脏骤停生物标志物的知识,并提出了未来的研究方向,旨在整合多个物组学数据层以改善预后预测和心脏骤停患者的护理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Intensive Care Medicine Experimental
Intensive Care Medicine Experimental CRITICAL CARE MEDICINE-
CiteScore
5.10
自引率
2.90%
发文量
48
审稿时长
13 weeks
期刊最新文献
Predictors of intradialytic hypotension in critically ill patients undergoing kidney replacement therapy: a systematic review. Is passive leg raising clinically useful in predicting intradialytic hypotension? Largely ignored-but pathogenetically significant: ambient temperature in rodent sepsis models. The development of a C5.0 machine learning model in a limited data set to predict early mortality in patients with ARDS undergoing an initial session of prone positioning. A new method to predict return of spontaneous circulation by peripheral intravenous analysis during cardiopulmonary resuscitation: a rat model pilot study.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1